Cameras are commonly used to capture an image of a scene that includes one or more objects. Unfortunately, some of the images are blurred. For example, movement of the camera, and/or movement of the objects in the scene during the exposure time of the camera can cause the image to be blurred. Further, the improper focus of the camera can cause the image to be blurred.
A blurred captured image can be modeled as the convolution of a latent sharp image with some point spread function (“PSF”) plus noise,B=K*L+N.  Equation (1)
In Equation 1 and elsewhere in this document, B denotes a blurry image, L a latent sharp image, K a PSF kernel, and N represents noise (including quantization errors, compression artifacts, etc.). The inverse problem of recovering both the latent sharp image L and the PSF kernel K when only the blurry image B is known, is called a blind deconvolution problem.
Many blurry images include areas that further complicate the problem of determining the PSF kernel K and the latent sharp image L. For example, certain areas of a blurry image B will have a different blur PSF kernel. Thus, it is often very difficult to accurately determine the PSF kernel K and the latent sharp image L of a blurry image.